Improvements in loss function in hyperparamter optiimization.

examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl

@@ -32,26 +32,44 @@ conf_train, conf_test = split(ds, n_train, n_test)
 
 # ## c. Hyper-parameter optimization.
 
-# Define the model and hyper-parameter value ranges to be optimized.
+# Define loss function. Here, we minimize error and time.
+function loss(metrics)
+    e_mae_max = 0.05
+    f_mae_max = 0.05
+    err       = metrics[:error] # weighted error: w_e * e_mae + w_f * f_mae
+    e_mae     = metrics[:e_mae]
+    f_mae     = metrics[:f_mae]
+    time_us   = metrics[:time_us]
+    if e_mae < e_mae_max && f_mae < f_mae_max
+       loss = time_us
+    else
+       loss = time_us + err * 10^3
+    end
+    return loss
+end
+
+# Define model and hyper-parameter value ranges to be optimized.
 model = ACE
 pars = OrderedDict( :body_order        => [2, 3, 4],
                     :polynomial_degree => [3, 4, 5],
                     :rcutoff           => [4.5, 5.0, 5.5],
                     :wL                => [0.5, 1.0, 1.5],
                     :csp               => [0.5, 1.0, 1.5],
                     :r0                => [0.5, 1.0, 1.5]);
+
 # Use random sampling to find the optimal hyper-parameters.
 iap, res = hyperlearn!(model, pars, conf_train; 
                        n_samples = 10, sampler = RandomSampler(),
                        ws = [1.0, 1.0], int = true)
 
+# Save and show results.
 @save_var res_path iap.β
 @save_var res_path iap.β0
 @save_var res_path iap.basis
 @save_dataframe res_path res
 res
 
-# Plot error vs time
+# Plot error vs time.
 err_time = plot_err_time(res)
 @save_fig res_path err_time
 DisplayAs.PNG(err_time)
@@ -62,13 +80,14 @@ iap, res = hyperlearn!(model, pars, conf_train;
                        n_samples = 3, sampler = LHSampler(),
                        ws = [1.0, 1.0], int = true)
 
+# Save and show results.
 @save_var res_path iap.β
 @save_var res_path iap.β0
 @save_var res_path iap.basis
 @save_dataframe res_path res
 res
 
-# Plot error vs time
+# Plot error vs time.
 err_time = plot_err_time(res)
 @save_fig res_path err_time
 DisplayAs.PNG(err_time)

examples/Opt-ACE-aHfO2/utils.jl

@@ -27,16 +27,6 @@ function get_results(ho)
 end
 
 # Plot fitting error vs force time (Pareto front)
-function plot_err_time(res)
-    error      = res[!, :error] 
-    times      = res[!, :time_us]
-    scatter(times,
-            error,
-            label = "",
-            xaxis = "Time per force per atom | µs",
-            yaxis = "we MAE(E, E') + wf MAE(F, F')")
-end
-
 function plot_err_time(res)
     e_mae = res[!, :e_mae]
     f_mae = res[!, :f_mae]
@@ -70,17 +60,6 @@ function plot_err_time(res)
 end
 
 
-function loss(p)
-    err, e_mae, f_mae, time_us = p[1], p[2], p[3], p[4]
-    e_mae_max, f_mae_max = 0.05, 0.05
-    if e_mae < e_mae_max && f_mae < f_mae_max
-       loss = time_us
-    else
-       loss = time_us + err * 10^3
-    end
-    return loss
-end
-
 function get_species(confs)
     return unique(vcat(unique.(atomic_symbol.(get_system.(confs)))...))
 end
@@ -126,7 +105,7 @@ function hyperlearn!(model, pars, conf_train;
                                 :f_rsq     => f_rsq,
                                 :time_us   => time_us)
         ## Compute multi-objetive loss based on error and time
-        l = loss([err, e_mae, f_mae, time_us])
+        l = loss(metrics)
         ## Print results
         print("E_MAE:$(round(e_mae; digits=3)), ")
         print("F_MAE:$(round(f_mae; digits=3)), ")